Abstract
This article presents a variational Bayes inference for normalized Gaussian network, which is a kind of mixture models of local experts. In order to search for the optimal model structure, we develop a hierarchical model selection method. The performance of our method is evaluated by using function approximation and nonlinear dynamical system identification problems. Our method achieved better performance than existing methods.
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Yoshimoto, J., Ishii, S., Sato, Ma. (2002). Hierarchical Model Selection for NGnet Based on Variational Bayes Inference. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_108
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DOI: https://doi.org/10.1007/3-540-46084-5_108
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